Create app.py

app.py

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+from fastapi import FastAPI
+import joblib
+import pandas as pd
+from pydantic import BaseModel
+
+# Load the trained model
+model = joblib.load("slope_stability_model.pkl")
+
+# Initialize FastAPI
+app = FastAPI()
+
+# Define request format using Pydantic
+class SlopeStabilityInput(BaseModel):
+    unit_weight: float
+    cohesion: float
+    friction_angle: float
+    slope_angle: float
+    slope_height: float
+    water_pressure_ratio: float
+    reinforcement_type: str
+
+# Define API endpoint
+@app.post("/predict")
+def predict_slope_stability(data: SlopeStabilityInput):
+    # Convert input data into a DataFrame
+    input_data = pd.DataFrame([data.dict()])
+
+    # Make prediction using the model
+    prediction = model.predict(input_data)
+
+    return {"Factor of Safety": float(prediction[0])}